TCM Physical Health Management Training and Nursing Effect Evaluation Based on Digital Twin

Abstract

Traditional Chinese medicine (TCM) has been shown to be effective in the treatment of diseases such as malaria, being better understood and accepted by the world. TCM physical health management is based on the policy of “preventive disease,” comprehensively collects patients’ information, and provides timely and appropriate rehabilitation guidance to achieve the best nursing effect. However, the current TCM physical health management has not been understood by the public, and the effect of its nursing evaluation has not been concluded yet. Therefore, this paper aims to design a TCM physical health management training and learning system based on digital twin technology and to evaluate and analyze the nursing effect. For TCM physical health management training, this paper designed a training system based on the VIKOR algorithm. Based on digital twin technology, the training can be carried out at different times and places, and the teaching content can also be displayed in real time through the cloud platform, which intuitively and comprehensively reflects the teaching content. For the evaluation of nursing effect, this paper selected 100 patients and divided them into two groups to compare the nursing effect of TCM physical health management and general Western medicine nursing. The experimental results of this paper found that the nursing effect of TCM physical health management is 20%–60% better than that of Western medicine nursing in terms of blood pressure, TCM syndromes, exercise tolerance, and quality of life.

The effectiveness of digital twins in promoting precision health across the entire population: a systematic review

“…Further scrutiny resulted in the exclusion of one study (1/17, 5.88%) lacking healthrelated outcomes and four studies (4/17, 23.53%) with overlapping data. Ultimately, 12 (12/17, 70.59%) original studies [13][14][15][16][17][18][19][20][21][22][23][24] were included in the systematic review. Supplementary Table 1 provides a summary of the reasons for exclusion at the full-text reading phase.…”

“…The studies included in this systematic review were published between 2021 (2/12, 16.67%) 23,24 and 2023 (8/12, 66.67%) [13][14][15][16][17][18][19][20] . Originating from diverse regions, 4/12 studies (33.33%) were from Asia 13,14,21,24 , 5/12 (41.67%) from America 15,17,19,20,22 , and 3/12 (25.00%) from Europe 16,18,23 . The review encompassed various study designs, including randomized controlled trials (1/12, 8.33%) 14 , quasi-experiments (6/12, 50.00%) 13,15,16,18,19,21 , and cohort studies (5/12, 41.67%) 17,20,[22][23][24] .…”

“…Originating from diverse regions, 4/12 studies (33.33%) were from Asia 13,14,21,24 , 5/12 (41.67%) from America 15,17,19,20,22 , and 3/12 (25.00%) from Europe 16,18,23 . The review encompassed various study designs, including randomized controlled trials (1/12, 8.33%) 14 , quasi-experiments (6/12, 50.00%) 13,15,16,18,19,21 , and cohort studies (5/12, 41.67%) 17,20,[22][23][24] . The sample sizes ranged from 15 13 to 3500 patients 19 .…”

“…The sample sizes ranged from 15 13 to 3500 patients 19 . Five studies assessed the impact of digital twins on virtual patients 15,16,[18][19][20] , while seven examined their effect on real-world patients 13,14,17,[21][22][23][24] . These patients included had various diseases, including cancer (4/12, 33.33%) 15,16,19,22 , type 2 diabetes (2/12, 16.66%) 13,14 , multiple sclerosis (2/12, 16.66%) 17,18 , qi deficiency (1/12, 8.33%) 21 , heart failure (1/12, 8.33%) 20 , post-hepatectomy liver failure (1/12, 8.33%) 23 , and dental issues (1/12, 8.33%) 24 .…”

“…Five studies assessed the impact of digital twins on virtual patients 15,16,[18][19][20] , while seven examined their effect on real-world patients 13,14,17,[21][22][23][24] . These patients included had various diseases, including cancer (4/12, 33.33%) 15,16,19,22 , type 2 diabetes (2/12, 16.66%) 13,14 , multiple sclerosis (2/12, 16.66%) 17,18 , qi deficiency (1/12, 8.33%) 21 , heart failure (1/12, 8.33%) 20 , post-hepatectomy liver failure (1/12, 8.33%) 23 , and dental issues (1/12, 8.33%) 24 . This review coded interventions into three types: personalized health management (3/12, 25.00%) 13,14,21 , precision individual therapy effects (3/12, 25.00%) 15,16,[18][19][20]22 , and predicting individual risk (3/12, 25.00%) 17,23,24 , with a total of 45 measured outcomes.…”